Meta-Llama-3-8B-Instruct
meta/llama-3-8b-instructمن Meta · العائلة: llama · أُصدِر 2025-04-03 · تاريخ المعرفة: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
القدرات
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding8
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability8/10
Agents8
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit0/15
- Provider availability8/10
JSON / structured output30
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume20/20
Cost efficiency90
- Headline price (log-scaled)90/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness89
- Number of independent providers40/40
- Has published per-token price20/20
- Context window ≥ 8K8/15
- No data inconsistencies across providers6/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.17 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $0.34 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.08 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $0.28 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $0.38 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
تفاصيل التسعير
السعر المُوصى به من kilo · meta-llama/llama-3-8b-instruct
أرخص مزود: github-models · Unknown إدخال + Unknown إخراج
متاح لدى 8 مزود
| المزود | معرف نموذج المزود | إدخال / 1M | إخراج / 1M | السياق | تاريخ الإصدار |
|---|---|---|---|---|---|
| Azure azure | meta-llama-3-8b-instruct | $0.300 | $0.610 | 8K | 2024-04-18 |
| NovitaAI novita-ai | meta-llama/llama-3-8b-instruct | $0.040 | $0.040 | 8K | 2024-04-25 |
| Kilo Gateway kilo | meta-llama/llama-3-8b-instruct | $0.030 | $0.040 | 8K | 2024-04-25 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-3-8b-instruct-awq | $0.120 | $0.270 | 128K | 2025-04-03 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-3-8b-instruct | $0.280 | $0.830 | 128K | 2025-04-03 |
| Azure Cognitive Services azure-cognitive-services | meta-llama-3-8b-instruct | $0.300 | $0.610 | 8K | 2024-04-18 |
| LLM Gateway llmgateway | llama-3-8b-instruct | $0.040 | $0.040 | 8K | 2025-04-03 |
| GitHub Models github-models | meta/meta-llama-3-8b-instruct | Unknown | Unknown | 8K | 2024-04-18 |
اختلافات في بيانات المزودين
- context_window varies: 128000, 8192
- release_date varies (span 350d): 2024-04-18, 2024-04-25, 2025-04-03
يبلِّغ المزودون قيمًا مختلفة لهذا النموذج. تستخدم الحقائق السريعة أعلاه مزودًا تمثيليًا؛ راجع الجدول للتفاصيل لكل مزود.
Frequently asked questions
How much does Meta-Llama-3-8B-Instruct cost?
Meta-Llama-3-8B-Instruct costs $0.030 per 1M input tokens and $0.040 per 1M output tokens, sourced from kilo. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Meta-Llama-3-8B-Instruct?
Meta-Llama-3-8B-Instruct has a context window of 8K tokens, with a max output of 2K tokens per reply. This is the total combined size of prompt + completion.
Does Meta-Llama-3-8B-Instruct support tool calling?
No. Meta-Llama-3-8B-Instruct does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Meta-Llama-3-8B-Instruct support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Meta-Llama-3-8B-Instruct in our data source. Verify with Meta's official documentation before relying on it in production.
Can Meta-Llama-3-8B-Instruct accept image input?
No. Meta-Llama-3-8B-Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Meta-Llama-3-8B-Instruct open-weight?
Yes. Meta-Llama-3-8B-Instruct's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Meta-Llama-3-8B-Instruct?
If Meta-Llama-3-8B-Instruct doesn't fit, consider Meta-Llama-3.1-8B-Instruct, Llama-3.3-70B-Instruct, Llama 4 Maverick 17B 128E Instruct FP8. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More Meta models
- Meta-Llama-3.1-8B-Instruct$0.02 in / $0.03 out
- Llama-3.3-70B-Instruct$0.05 in / $0.23 out
- Llama 4 Maverick 17B 128E Instruct FP8$0.14 in / $0.59 out
- Llama 4 Scout 17B 16E Instruct$0.08 in / $0.30 out
- Meta-Llama-3.1-70B-Instruct$0.40 in / $0.40 out
Capability lists this model is in
آخر تحديث:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.